PEI103
INTELLIGENT CONTROL TECHNIQUES AND APPLICATION 


L 
T 
P 
Cr 

3 
0 
2 
4.0 
Prerequisite(s): None 
Overview of Artificial Intelligence and
Expert Systems: The concept and
importance of AI, General Concepts of Expert System
Artificial Neural Networks: Artificial Neuron models, Types of activation
functions, Neural network architectures, Neural Learning: Correlation,
Competitive, Feedback based weight adaptation, Evaluation of networks, Quality
of results, Generalizability, Computational
resources, Supervised learning: Perceptrons, linear separability, Multilayer networks, Backpropagation
algorithm and its varianta, Unsupervised learning,
Winnertake all networks, Adaptive
resonance theory, Self organizing maps, Hopfield networks, Boltzman machines, Support Vector Machine, Typical
application in identification, Optimization, Pattern recognition etc.
Fuzzy Logic: Fuzziness vs probability,
Crisp logic vs fuzzy logic, Fuzzy sets and systems,
Operations on sets, Fuzzy relations, Membership functions, Fuzzy rule
generation, De fuzzy controllers, Type2 Fuzzy Logic Controllers, Multilayer and other advanced Fuzzy Logic Models,
Applications of Fuzzy Logic.
Evolutionary Computation: Introduction
and concept as a process modeling tool, Genetic Algorithms and its Operators,
Binary Coded Genetic Algorithm and its use in Engineering Process Modeling, Unimodal vs Multimodal problems
in GA and their significance, Applications.
Hybrid Techniques: Neurofuzzy systems, FuzzyExpert system, FuzzyGA systems
Applications: Applications of Intelligent techniques in Process control, Robotics and other
industrial control methods.
Laboratory Work: Experiments around Input
and output using Fuzzy logic, Graphical analysis of various control systems
using Fuzzy logic, Dynamical and optimal training for neural networks,
Experiments around GA.
Recommended
Books